fix: harden reviewer workflow and memory handling

deploy/compose.psd-local.yaml.example

@@ -32,6 +32,10 @@ services:
       - ${THT_PSD_WORKSPACE_HOST_PATH:?set THT_PSD_WORKSPACE_HOST_PATH}:/data/workspaces/psd
 
   frontend:
+    build:
+      args:
+        VITE_BASE: /
+        VITE_BACKEND_URL: /api
     ports:
       - "127.0.0.1:8099:8080"
     networks: !override

frontend/src/widgets/MultiselectWidget.test.tsx

@@ -46,6 +46,56 @@ test("uses memory-specific selection copy without changing selected ids", async
   expect(onRespond).toHaveBeenCalledWith({ id: "u-memory", kind: "multiselect", choices: ["recommended"] });
 });
 
+test("shows the exact content and provenance for every memory option", () => {
+  render(
+    <MultiselectWidget
+      descriptor={{
+        id: "u-memory-content",
+        widget: "multiselect",
+        options: [
+          {
+            id: "mem-42",
+            label: "paziente attivo",
+            detail: "flag_attivo = TRUE",
+            rationale: "Confermato dal reviewer",
+            meta: { question_context: "Conta i pazienti attivi" },
+          },
+        ],
+      }}
+      onRespond={vi.fn()}
+    />
+  );
+
+  expect(screen.getByText("flag_attivo = TRUE")).toBeInTheDocument();
+  expect(screen.getByText(/Confermato dal reviewer/)).toBeInTheDocument();
+  expect(screen.getByText(/Conta i pazienti attivi/)).toBeInTheDocument();
+});
+
+test("renders memory detail as human-friendly Markdown", () => {
+  render(
+    <MultiselectWidget
+      descriptor={{
+        id: "u-memory-markdown",
+        widget: "multiselect",
+        selection_label: "memory da applicare",
+        options: [
+          {
+            id: "mem-0042",
+            label: "Paziente attivo",
+            detail: "**Definizione**\n\n- `flag_attivo = TRUE`\n- record non annullato",
+          },
+        ],
+      }}
+      onRespond={vi.fn()}
+    />
+  );
+
+  expect(screen.getByText("Definizione").tagName).toBe("STRONG");
+  expect(screen.getByText("flag_attivo = TRUE").tagName).toBe("CODE");
+  expect(screen.getByText("record non annullato").closest("li")).not.toBeNull();
+  expect(screen.queryByText("**Definizione**")).not.toBeInTheDocument();
+});
+
 test("select-all checks all options", async () => {
   const onRespond = vi.fn();
   render(

frontend/src/widgets/MultiselectWidget.tsx

@@ -1,4 +1,6 @@
 import { useState } from "react";
+import ReactMarkdown from "react-markdown";
+import remarkGfm from "remark-gfm";
 import type { WidgetProps } from "./types";
 import { ReservedControls } from "./ReservedControls";
 
@@ -56,7 +58,24 @@ export function MultiselectWidget({ descriptor, onRespond }: WidgetProps) {
                 checked={checked.has(o.id)}
                 onChange={() => toggle(o.id)}
               />
-              <span>{o.label}</span>
+              <span className="min-w-0 flex-1">
+                <span className="block font-medium">{o.label}</span>
+                {o.detail && (
+                  <div className="thot-prose mt-1 text-sm text-muted-foreground">
+                    <ReactMarkdown remarkPlugins={[remarkGfm]}>{o.detail}</ReactMarkdown>
+                  </div>
+                )}
+                {o.rationale && (
+                  <span className="mt-1 block text-xs text-foreground/80">
+                    Motivo: {o.rationale}
+                  </span>
+                )}
+                {typeof o.meta?.question_context === "string" && o.meta.question_context && (
+                  <span className="mt-1 block text-xs text-muted-foreground">
+                    Domanda di contesto: {o.meta.question_context}
+                  </span>
+                )}
+              </span>
             </label>
           ))}
         </div>

frontend/src/widgets/SchemaLinkingGateWidget.test.tsx

@@ -33,6 +33,13 @@ const descriptor: WidgetDescriptor = {
   reserved: ["back", "exit", "other"],
 };
 
+test("starts include rows checked and exclude rows unchecked even for legacy payloads", () => {
+  render(<SchemaLinkingGateWidget descriptor={descriptor} onRespond={vi.fn()} />);
+
+  expect(screen.getByRole("checkbox", { name: "dim_patient" })).toBeChecked();
+  expect(screen.getByRole("checkbox", { name: "fact_sostituzione" })).not.toBeChecked();
+});
+
 test("Confirm emits enacted tables with suggested columns (catalog order)", async () => {
   const onRespond = vi.fn();
   render(<SchemaLinkingGateWidget descriptor={descriptor} onRespond={onRespond} />);
@@ -42,7 +49,7 @@ test("Confirm emits enacted tables with suggested columns (catalog order)", asyn
     kind: "schema-linking",
     tables: [
       { id: "t-pat", enacted: true, columns: ["cod_paz"] },
-      { id: "x-sub", enacted: true },
+      { id: "x-sub", enacted: false },
     ],
   });
 });
@@ -60,7 +67,7 @@ test("selecting a column in the modal adds it to the response", async () => {
     kind: "schema-linking",
     tables: [
       { id: "t-pat", enacted: true, columns: ["cod_paz", "nome"] },
-      { id: "x-sub", enacted: true },
+      { id: "x-sub", enacted: false },
     ],
   });
 });
@@ -75,7 +82,7 @@ test("declining a table's enact checkbox drops its columns", async () => {
     kind: "schema-linking",
     tables: [
       { id: "t-pat", enacted: false },
-      { id: "x-sub", enacted: true },
+      { id: "x-sub", enacted: false },
     ],
   });
 });

frontend/src/widgets/SchemaLinkingGateWidget.tsx

@@ -7,7 +7,7 @@ import { ReservedControls } from "./ReservedControls";
 export function SchemaLinkingGateWidget({ descriptor, onRespond }: WidgetProps) {
   const tables: SchemaTable[] = descriptor.tables ?? [];
   const [enacted, setEnacted] = useState<Set<string>>(
-    () => new Set(tables.filter((t) => t.recommended).map((t) => t.id))
+    () => new Set(tables.filter((t) => t.kind === "promote" && t.recommended).map((t) => t.id))
   );
   const [selected, setSelected] = useState<Map<string, Set<string>>>(() => {
     const m = new Map<string, Set<string>>();

harness/.pi/extensions/gate/__tests__/gate_datamart_choice.test.js

@@ -0,0 +1,104 @@
+const test = require("node:test");
+const assert = require("node:assert");
+const cp = require("node:child_process");
+const { createRequire } = require("node:module");
+const path = require("node:path");
+
+const GATE = path.join(__dirname, "..", "..", "tht-gate.js");
+if (typeof globalThis.require === "undefined") {
+  globalThis.require = createRequire(GATE);
+}
+
+let activeStub = cp.execFileSync;
+const dispatcher = (...args) => activeStub(...args);
+Object.defineProperty(cp, "execFileSync", {
+  configurable: true,
+  get: () => dispatcher,
+  set: (fn) => { activeStub = fn; },
+});
+
+async function loadGate() {
+  const gate = require(GATE);
+  const { createFakePi } = require("./fake_pi_runtime.js");
+  const { pi, ctx, tools } = createFakePi();
+  ctx.cwd = "/nonexistent-thothii-test-cwd";
+  gate.default(pi);
+  await pi.emit("session_start", {});
+  return { ctx, tools };
+}
+
+function phase8Stub(calls) {
+  return (_file, args) => {
+    calls.push(args);
+    if (args[0] === "phase" && args[1] === "meta") {
+      return JSON.stringify({
+        max_phase: 8,
+        phases: [{
+          num: 8,
+          id: "F8",
+          name: "datamart",
+          emits: ["datamart_requested", "datamart_declined"],
+        }],
+      });
+    }
+    if (args[0] === "phase" && args[1] === "show") return "Fase corrente: 8\n";
+    return "";
+  };
+}
+
+test("reviewer_datamart skips the question and records decline on workstation", async () => {
+  const previousProfile = process.env.THT_PROFILE;
+  const originalExec = cp.execFileSync;
+  const calls = [];
+  process.env.THT_PROFILE = "workstation";
+  cp.execFileSync = phase8Stub(calls);
+  try {
+    const { ctx, tools } = await loadGate();
+    const tool = tools.get("reviewer_datamart");
+    assert.ok(tool, "reviewer_datamart must be registered");
+
+    const result = await tool.def.execute("call-1", { session: "s1" }, null, null, ctx);
+
+    assert.equal(ctx.uiCalls.length, 0, "workstation must not show a pointless question");
+    assert.ok(calls.some((args) =>
+      args.join(" ").includes("decision add --session s1 --type datamart_declined --subject phase:8")
+    ));
+    assert.match(result.content[0].text, /workstation.*saltato automaticamente/i);
+  } finally {
+    cp.execFileSync = originalExec;
+    if (previousProfile === undefined) delete process.env.THT_PROFILE;
+    else process.env.THT_PROFILE = previousProfile;
+  }
+});
+
+test("reviewer_datamart shows both yes and no choices on server", async () => {
+  const previousProfile = process.env.THT_PROFILE;
+  const originalExec = cp.execFileSync;
+  const calls = [];
+  process.env.THT_PROFILE = "server";
+  cp.execFileSync = phase8Stub(calls);
+  try {
+    const { ctx, tools } = await loadGate();
+    ctx.ui.input = async (title) => {
+      const descriptor = JSON.parse(title);
+      assert.deepEqual(
+        descriptor.options.slice(0, 2).map((option) => option.label),
+        ["Sì, genera il datamart", "No, salta il datamart"],
+      );
+      return JSON.stringify({ id: descriptor.id, choices: ["generate"] });
+    };
+
+    const result = await tools.get("reviewer_datamart").def.execute(
+      "call-1", { session: "s1" }, null, null, ctx,
+    );
+
+    assert.ok(calls.some((args) =>
+      args.join(" ").includes("decision add --session s1 --type datamart_requested --subject phase:8")
+    ));
+    assert.match(result.content[0].text, /datamart_requested/);
+  } finally {
+    cp.execFileSync = originalExec;
+    if (previousProfile === undefined) delete process.env.THT_PROFILE;
+    else process.env.THT_PROFILE = previousProfile;
+  }
+});

harness/.pi/extensions/gate/__tests__/gate_memory_promote.test.js

@@ -1,6 +1,7 @@
 const test = require("node:test");
 const assert = require("node:assert");
 const {
+  dedupePromotionCandidates,
   promotionOptions,
   promotionContent,
   splitPromotionChoices,
@@ -20,26 +21,47 @@ const CANDIDATES = [
 ];
 
 test("promotionOptions maps candidates to seq-keyed options", () => {
-  assert.deepEqual(promotionOptions(CANDIDATES), [
-    { id: "seq-3", label: "table_promoted: fact_seeablazione" },
-    { id: "seq-5", label: "concept_clarified: paziente attivo" },
+  assert.deepEqual(promotionOptions(dedupePromotionCandidates(CANDIDATES)), [
+    {
+      id: "seq-5",
+      label: "concept_clarified: paziente attivo",
+      detail: "flag_attivo = TRUE",
+      rationale: "",
+      meta: { question_context: "quante ablazioni nel 2023" },
+    },
   ]);
 });
 
-test("promotionContent lists every candidate with detail and context", () => {
-  const c = promotionContent(CANDIDATES);
-  assert.ok(c.includes("fact_seeablazione"));
+test("F8 proposes semantically identical memory content only once", () => {
+  const duplicate = { ...CANDIDATES[0], decision_seq: 9 };
+  assert.deepEqual(
+    dedupePromotionCandidates([CANDIDATES[0], duplicate, CANDIDATES[1]])
+      .map((candidate) => candidate.decision_seq),
+    [5],
+  );
+});
+
+test("F8 never proposes promoted tables as memory", () => {
+  const candidates = dedupePromotionCandidates(CANDIDATES);
+  const options = promotionOptions(candidates);
+  const c = promotionContent(candidates);
+
+  assert.deepEqual(candidates.map((candidate) => candidate.type), ["concept_clarified"]);
+  assert.deepEqual(options.map((option) => option.id), ["seq-5"]);
+  assert.ok(!c.includes("fact_seeablazione"));
   assert.ok(c.includes("flag_attivo = TRUE"));
   assert.ok(c.includes("quante ablazioni nel 2023"));
 });
 
 test("splitPromotionChoices partitions by selection", () => {
-  const { promote, decline } = splitPromotionChoices(CANDIDATES, ["seq-5"]);
+  const candidates = dedupePromotionCandidates(CANDIDATES);
+  const { promote, decline } = splitPromotionChoices(candidates, ["seq-5"]);
   assert.deepEqual(promote.map((c) => c.decision_seq), [5]);
-  assert.deepEqual(decline.map((c) => c.decision_seq), [3]);
+  assert.deepEqual(decline.map((c) => c.decision_seq), []);
 });
 
 test("empty or missing choices declines everything", () => {
-  assert.equal(splitPromotionChoices(CANDIDATES, []).decline.length, 2);
-  assert.equal(splitPromotionChoices(CANDIDATES, undefined).decline.length, 2);
+  const candidates = dedupePromotionCandidates(CANDIDATES);
+  assert.equal(splitPromotionChoices(candidates, []).decline.length, 1);
+  assert.equal(splitPromotionChoices(candidates, undefined).decline.length, 1);
 });

harness/.pi/extensions/gate/__tests__/gate_memory_selection.test.js

@@ -1,6 +1,20 @@
 const test = require("node:test");
 const assert = require("node:assert");
-const { memorySelectionWidgetProps } = require("../../tht-gate.js");
+const path = require("node:path");
+const {
+  memorySelectionWidgetProps,
+  normalizeMemoryOptions,
+} = require("../../tht-gate.js");
+
+test("reviewer_decide accepts the exact memory content in option.description", () => {
+  const { createFakePi } = require("./fake_pi_runtime.js");
+  const { pi, tools } = createFakePi();
+  require(path.join(__dirname, "..", "..", "tht-gate.js")).default(pi);
+
+  const optionProperties = tools.get("reviewer_decide")
+    .def.parameters.properties.options.items.properties;
+  assert.ok(optionProperties.description);
+});
 
 test("F2 preseleziona solo le memory raccomandate e parla di applicazione", () => {
   assert.deepEqual(
@@ -16,3 +30,65 @@ test("F2 preseleziona solo le memory raccomandate e parla di applicazione", () =
     },
   );
 });
+
+test("F2 preserves the exact memory content and proposes each memory id once", () => {
+  const options = normalizeMemoryOptions([
+    {
+      id: "first",
+      label: "Regola paziente attivo",
+      description: "Decisione concept_clarified: paziente attivo\nflag_attivo = TRUE",
+      decision: {
+        type: "concept_clarified",
+        subject: "paziente attivo",
+        detail: "flag_attivo = TRUE",
+        rationale: "Riusa mem-0042 per la stessa definizione",
+      },
+      recommended: true,
+    },
+    {
+      id: "duplicate",
+      label: "La stessa regola",
+      description: "Decisione concept_clarified: paziente attivo\nflag_attivo = TRUE",
+      decision: {
+        type: "concept_clarified",
+        subject: "paziente attivo",
+        detail: "flag_attivo = TRUE",
+        rationale: "Memory mem-0042",
+      },
+    },
+  ]);
+
+  assert.equal(options.length, 1);
+  assert.equal(
+    options[0].detail,
+    "Decisione concept_clarified: paziente attivo\nflag_attivo = TRUE",
+  );
+  assert.equal(options[0].recommended, true);
+});
+
+test("F2 accepts only concept_clarified memory options", () => {
+  const options = normalizeMemoryOptions([
+    {
+      id: "mem-0042",
+      label: "Concetto paziente attivo",
+      description: "Definizione riusabile",
+      decision: {
+        type: "concept_clarified",
+        subject: "paziente attivo",
+        detail: "flag_attivo = TRUE",
+      },
+    },
+    {
+      id: "mem-0043",
+      label: "Tabella promossa",
+      description: "fact_pazienti",
+      decision: {
+        type: "table_promoted",
+        subject: "fact_pazienti",
+        detail: "tabella principale",
+      },
+    },
+  ]);
+
+  assert.deepEqual(options.map((option) => option.id), ["mem-0042"]);
+});

harness/.pi/extensions/gate/__tests__/gate_schema_linking.test.js

@@ -132,6 +132,64 @@ test("reviewer_schema_linking records table/column decisions and syncs schema_li
   }
 });
 
+test("reviewer_schema_linking preselects promoted tables but not excluded tables", async () => {
+  _handler = (_file, args) => {
+    if (args[0] === "phase" && args[1] === "meta")
+      return JSON.stringify({ phases: [{ num: 4, id: "F4" }] });
+    if (args[0] === "phase" && args[1] === "show") return "Fase corrente: 4\n";
+    if (args[0] === "schema" && args[1] === "columns")
+      return JSON.stringify({ ...CATALOG, table: args[2] });
+    return "";
+  };
+
+  try {
+    const gate = require(GATE);
+    const { createFakePi } = require("./fake_pi_runtime.js");
+    const { pi, ctx, tools } = createFakePi();
+    ctx.cwd = "/nonexistent-thothii-table-defaults";
+    gate.default(pi);
+
+    let capturedDescriptor;
+    ctx.ui.input = async (title) => {
+      capturedDescriptor = JSON.parse(title);
+      return JSON.stringify({
+        id: capturedDescriptor.id,
+        kind: "schema-linking",
+        tables: capturedDescriptor.tables.map((table) => ({
+          id: table.id,
+          enacted: table.recommended,
+          columns: [],
+        })),
+      });
+    };
+
+    await tools.get("reviewer_schema_linking").def.execute(
+      "call-defaults",
+      {
+        session: "s1",
+        title: "Schema linking",
+        tables: [
+          { id: "keep", name: "fact_keep", kind: "promote" },
+          { id: "drop", name: "fact_drop", kind: "exclude", recommended: true },
+        ],
+      },
+      null,
+      null,
+      ctx,
+    );
+
+    assert.deepEqual(
+      capturedDescriptor.tables.map(({ id, recommended }) => ({ id, recommended })),
+      [
+        { id: "keep", recommended: true },
+        { id: "drop", recommended: false },
+      ],
+    );
+  } finally {
+    _handler = null;
+  }
+});
+
 test("reviewer_schema_linking auto-corrects typos in table names via fuzzy matching", async () => {
   const calls = [];
   const inputs = [];

harness/.pi/extensions/tht-gate.js

@@ -573,13 +573,85 @@ export function memorySelectionWidgetProps(options) {
 	};
 }
 
+const MEMORY_ID_RE = /\bmem-\d{4,}\b/i;
+
+function normalizedText(value) {
+	return String(value ?? "").trim().replace(/\s+/g, " ").toLowerCase();
+}
+
+function memoryOptionKey(option) {
+	const searchable = [
+		option.id,
+		option.label,
+		option.description,
+		option.decision?.rationale,
+	].join(" ");
+	const memoryId = searchable.match(MEMORY_ID_RE)?.[0]?.toLowerCase();
+	if (memoryId) return `id:${memoryId}`;
+	return [
+		"content",
+		normalizedText(option.decision?.type),
+		normalizedText(option.decision?.subject),
+		normalizedText(option.decision?.detail),
+	].join(":");
+}
+
+// F2 is model-orchestrated, so normalize at the trust boundary: retain the exact
+// retrieved memory text for the reviewer and collapse repeated representations of
+// the same memory id/content before rendering or persisting a choice.
+export function normalizeMemoryOptions(options) {
+	const seen = new Set();
+	const out = [];
+	for (const option of options) {
+		if (option.decision?.type !== "concept_clarified") continue;
+		const key = memoryOptionKey(option);
+		if (seen.has(key)) continue;
+		seen.add(key);
+		const memoryId = [option.id, option.label, option.description, option.decision?.rationale]
+			.join(" ").match(MEMORY_ID_RE)?.[0]?.toLowerCase();
+		const fallbackDetail = [
+			option.decision?.type && option.decision?.subject
+				? `Decisione ${option.decision.type}: ${option.decision.subject}`
+				: "",
+			option.decision?.detail ?? "",
+		].filter(Boolean).join("\n");
+		out.push({
+			...option,
+			detail: option.description?.trim() || fallbackDetail,
+			rationale: option.decision?.rationale ?? "",
+			...(memoryId ? { meta: { memory_id: memoryId } } : {}),
+		});
+	}
+	return out;
+}
+
 // --- F8 memory-promotion gate: pure candidate->widget mapping (L1-tested) ------
 // The candidates come from `tht memory promote --preview --json` (deterministic,
 // reviewer-approved decisions only); the model never authors them.
+export function dedupePromotionCandidates(candidates) {
+	const seen = new Set();
+	return candidates.filter((candidate) => {
+		if (candidate.type !== "concept_clarified") return false;
+		const key = [
+			candidate.type,
+			candidate.subject,
+			candidate.detail,
+			candidate.rationale,
+			candidate.question_context,
+		].map(normalizedText).join("\u0000");
+		if (seen.has(key)) return false;
+		seen.add(key);
+		return true;
+	});
+}
+
 export function promotionOptions(candidates) {
 	return candidates.map((c) => ({
 		id: `seq-${c.decision_seq}`,
 		label: `${c.type}: ${c.subject}`,
+		detail: c.detail || "",
+		rationale: c.rationale || "",
+		meta: { question_context: c.question_context || "" },
 	}));
 }
 
@@ -795,7 +867,7 @@ export default function (pi) {
 		}
 	});
 
-	// --- the four reviewer tools (widget-descriptor emit + await) ---------------
+	// --- reviewer tools (widget-descriptor emit + await) -------------------------
 
 	pi.registerTool({
 		name: "reviewer_select",
@@ -881,6 +953,87 @@ export default function (pi) {
 		},
 	});
 
+	pi.registerTool({
+		name: "reviewer_datamart",
+		label: "Scelta datamart (reviewer)",
+		description:
+			"F8: gestisce deterministicamente la scelta di generazione del datamart. " +
+			"Con THT_PROFILE=workstation non mostra alcun widget e registra automaticamente " +
+			"datamart_declined; con profile=server mostra sempre entrambe le opzioni Si/No e " +
+			"registra la decisione scelta. Dopo questo tool prosegui con reviewer_memory_promote.",
+		parameters: Type.Object({
+			session: Type.String(),
+		}),
+		async execute(_id, params, _signal, _onUpdate, ctx) {
+			lockActive = true;
+			try {
+				const { session } = params;
+				const curNum = currentPhase(ctx, session);
+				if (curNum !== 8) {
+					return textResult(
+						`La scelta datamart e' disponibile solo in Fase 8 (fase corrente: ${curNum}).`,
+					);
+				}
+				const subject = "phase:8";
+				if (process.env.THT_PROFILE === "workstation") {
+					const decision = { type: "datamart_declined", subject };
+					const err = relayIfThtFails(ctx, decisionAddArgs(session, decision), "");
+					if (err) return err;
+					return textResult(
+						"Profilo workstation: datamart saltato automaticamente " +
+						"e decisione datamart_declined registrata. Invoca ora reviewer_memory_promote.",
+					);
+				}
+
+				const options = [
+					{
+						id: "generate",
+						label: "Sì, genera il datamart",
+						decision: { type: "datamart_requested", subject },
+					},
+					{
+						id: "skip",
+						label: "No, salta il datamart",
+						decision: { type: "datamart_declined", subject },
+						recommended: true,
+					},
+				];
+				const widget = buildSelectRequest({
+					id: `u${Date.now()}`,
+					phase: phaseId(ctx, curNum),
+					title: "Vuoi generare un datamart?",
+					intro: null,
+					recommended: "skip",
+					options: options.map((option) => ({ id: option.id, label: option.label })),
+				});
+				const resp = await emitAndWait(ctx, widget);
+				const outcome = resolveSelectOutcome(options, resp);
+				if (outcome.kind === "freetext")
+					return textResult(`Altro (reviewer): ${outcome.text}`);
+				if (outcome.kind === "back")
+					return textResult("Il reviewer vuole tornare indietro.");
+				if (outcome.kind === "exit")
+					return textResult("Il reviewer vuole uscire.");
+				if (outcome.kind !== "decision")
+					return textResult("Scelta datamart non valida: ripresenta reviewer_datamart.");
+				const err = relayIfThtFails(
+					ctx,
+					decisionAddArgs(session, outcome.decision),
+					"",
+				);
+				if (err) return err;
+				return textResult(
+					decisionRecordedResultText(outcome.decision, outcome.option),
+				);
+			} catch (fatal) {
+				const msg = (fatal.stderr || fatal.message || String(fatal)).toString().trim();
+				return textResult(
+					`[reviewer_datamart ERRORE INTERNO] ${msg}. Riprova o usa un approccio diverso.`,
+				);
+			}
+		},
+	});
+
 	pi.registerTool({
 		name: "reviewer_decide",
 		label: "Decisione di merito (reviewer)",
@@ -895,6 +1048,7 @@ export default function (pi) {
 				Type.Object({
 					id: Type.String(),
 					label: Type.String(),
+					description: Type.Optional(Type.String()),
 					decision: Type.Object({
 						type: Type.String(),
 						subject: Type.String(),
@@ -911,14 +1065,23 @@ export default function (pi) {
 		async execute(_id, params, _signal, _onUpdate, ctx) {
 			lockActive = true;
 			try {
-			const { session, title, options: opts, advance } = params;
+			const { session, title, advance } = params;
+			const phase = phaseId(ctx, currentPhase(ctx, session));
+			const opts = phase === "F2"
+				? normalizeMemoryOptions(params.options)
+				: params.options;
 			const typeErr = validateDecisionTypes(ctx, opts, session);
 			if (typeErr) return textResult(typeErr);
-			const phase = phaseId(ctx, currentPhase(ctx, session));
 			const toAdd = [];
 			const meritOptions = opts
 				.filter((o) => !isReserved(o.label))
-				.map((o) => ({ id: o.id, label: o.label }));
+				.map((o) => ({
+					id: o.id,
+					label: o.label,
+					...(o.detail ? { detail: o.detail } : {}),
+					...(o.rationale ? { rationale: o.rationale } : {}),
+					...(o.meta ? { meta: o.meta } : {}),
+				}));
 			const joinOnly = meritOptions.length > 0 && opts
 				.filter((o) => !isReserved(o.label))
 				.every((o) => o.decision.type === "join_modified");
@@ -1084,7 +1247,7 @@ export default function (pi) {
 					id: t.id,
 					name: t.name,
 					kind: t.kind,
-					recommended: t.recommended ?? true,
+					recommended: t.kind === "promote" ? (t.recommended ?? true) : false,
 					description: cat.description ?? "",
 					rationale: t.rationale ?? "",
 					columns: cat.columns.map((c) => ({ ...c, suggested: suggested.has(c.name) })),
@@ -1529,8 +1692,8 @@ export default function (pi) {
 		label: "Promozione memorie riusabili (reviewer)",
 		description:
 			"F8 (prima della chiusura di fase): propone al reviewer i candidati di promozione " +
-			"calcolati dalla CLI (tht memory promote --preview: tipi riusabili concept_clarified/" +
-			"table_promoted/table_excluded, max 5, esclusi i gia' promossi/rifiutati). Le selezioni " +
+			"calcolati dalla CLI (tht memory promote --preview: solo concept_clarified, " +
+			"max 5, esclusi i gia' promossi/rifiutati). Le selezioni " +
 			"vengono salvate nel vectordb (tht memory save-one) e registrate come memory_promoted; " +
 			"le deselezioni come memory_promotion_declined (non riproposte). Nessun parametro oltre " +
 			"alla sessione: i candidati sono deterministici, NON li scrivi tu. Registrata la " +
@@ -1567,11 +1730,25 @@ export default function (pi) {
 					"Nessun candidato di promozione: prosegui con la chiusura della sessione.",
 				);
 			}
+			candidates = dedupePromotionCandidates(candidates);
+			if (candidates.length === 0) {
+				await ctx.ui.notify(
+					"Nessun concetto chiarito da salvare come memory per questa sessione.",
+					"info",
+				);
+				const closed = closeAfterPromotion(
+					ctx, session, curNum, "Nessun candidato di promozione.",
+				);
+				if (closed) return closed;
+				return textResult(
+					"Nessun candidato di promozione: prosegui con la chiusura della sessione.",
+				);
+			}
 			const options = promotionOptions(candidates);
 			const widget = buildMultiselectRequest({
 				id: `u${Date.now()}`,
 				phase,
-				title: "Quali decisioni salvare nella memoria riutilizzabile?",
+				title: "Quali concetti chiariti salvare nella memoria riutilizzabile?",
 				allowEmpty: true,
 				options,
 				selected: options.map((o) => o.id),

harness/.pi/skills/tht-sessione/SKILL.md

@@ -225,11 +225,15 @@ Prerequisite: Phase 1 closed.
 2. The hit comes with full metadata (subject/detail/rationale): read what it says,
    where it comes from, why it might apply here, the out-of-context risk.
 3. Present candidates in **a single** `reviewer_decide(multi:true, advance:true,
-   allow_empty:true)`. Rules: at most **5** candidates; ONLY the 3 reusable types
-   (`concept_clarified`, `table_promoted`, `table_excluded`) — query-specific
-   decisions (`question_rewritten`, `sql_approved`, …) are NOT transferable, never
-   propose them. Each option carries `type`/`subject`/`rationale`; cite the source
-   memory id (`mem-<id>`) in its rationale when applying it. Every option describes a
+   allow_empty:true)`. Rules: at most **5** candidates; ONLY
+   `concept_clarified`. Table choices (`table_promoted`, `table_excluded`) and all
+   other query-specific decisions (`question_rewritten`, `sql_approved`, …) are NOT
+   transferable and must never be stored, retrieved, or proposed as memories. Each
+   option carries `type`/`subject`/`rationale`; cite the source
+   memory id (`mem-<id>`) in its rationale when applying it. Copy the hit's full
+   `content` verbatim into the option `description`: the reviewer must see the exact
+   memory text before deciding. Deduplicate hits by memory id before calling the gate.
+   Every option describes a
    candidate memory; never create an opposite "do not use" option. Only
    `recommended:true` options start checked. A
    deselected candidate is **not applied now**, not rejected, and may be considered
@@ -411,8 +415,15 @@ Prerequisite: Phase 6 closed.
 
 Prerequisite: Phase 7 closed.
 
-1. Ask the reviewer whether they want a datamart (`reviewer_select` yes/no).
-2. If yes: `tht datamart generate` (stub — raises NotImplementedError for now). Tell
+1. Call `reviewer_datamart` with the session id. This gate is deployment-aware and is
+   the ONLY allowed way to record the datamart choice:
+   - `THT_PROFILE=workstation`: it records `datamart_declined` automatically and shows
+     no question to the reviewer;
+   - `THT_PROFILE=server` (including the default): it always shows both choices,
+     "Sì, genera il datamart" and "No, salta il datamart", and records the selected one.
+   Never replace this gate with a hand-built `reviewer_select`.
+2. On a server, if the reviewer chose yes: `tht datamart generate` (stub — raises
+   NotImplementedError for now). Tell
    the reviewer that dbt generation is not implemented yet.
 3. **Memory promotion closes the session.** Call `reviewer_memory_promote` with ONLY
    the session id: the gate computes the candidates itself (`tht memory promote

harness/.pi/skills/tht-sessione/memoria.md

@@ -9,8 +9,7 @@ already discarded (even after a Phase 2 reopen).
 For each memory to present in the checklist, include in the option's `label` and/or
 `description`:
 
-- **What it says**: type + subject + detail (e.g. "table_promoted:
-  fact_seeablazione — main table for ablazioni").
+- **What it says**: the clarified concept, its subject, and its full definition.
 - **Where it comes from**: question_context and origin session_id.
 - **Why it might apply here**: overlap of concepts/tables with the current question
   (fields tables/concepts), similarity score.
@@ -19,10 +18,10 @@ For each memory to present in the checklist, include in the option's `label` and
 
 Rules:
 
-- Propose at most **5** candidates. Include ONLY memories of the 3 reusable types:
-  `concept_clarified`, `table_promoted`, `table_excluded`. Query-specific decisions
-  (e.g. `question_rewritten`, `sql_approved`) are NOT to be proposed: they don't
-  transfer to other questions.
+- Propose at most **5** candidates. Include ONLY `concept_clarified` memories.
+  Table choices (`table_promoted`, `table_excluded`) and all other query-specific
+  decisions are not memories: never store, retrieve, or propose them because they
+  do not transfer to other questions.
 - All candidate memories go in **a single** `reviewer_decide(multi:true,
   advance:true, allow_empty:true)`: every option describes a candidate memory, never
   an opposite action such as "do not use it". Each selected option is applied

harness/tests/l2/test_memory_save_one_real.py

@@ -34,9 +34,10 @@ def test_save_one_upserts_to_real_pgvector(l2_env):
 
     record = MemoryRecord(
         id="mem-l2test", ts=datetime.now(), session_id="l2-self-test",
-        decision_seq=999, type="table_promoted", subject="fct_ricoveri",
-        detail="ablazione", rationale="L2 self-test (idempotent)",
-        question_context="ablazione 2025", tables=["fct_ricoveri"], concepts=[],
+        decision_seq=999, type="concept_clarified", subject="ablazione recente",
+        detail="evento di ablazione negli ultimi 15 anni",
+        rationale="L2 self-test (idempotent)",
+        question_context="ablazione 2025", tables=[], concepts=["ablazione recente"],
     )
     from tht.adapters.vector import ThothHttpVectorStore
 

harness/tests/test_adapter_command_regressions.py

@@ -68,9 +68,11 @@ def test_memory_command_writes_through_factory_vector_store(monkeypatch):
     cfg = SimpleNamespace(profile="server", embeddings=object(), vector_write_rest=None)
     manifest = SimpleNamespace(id="s1")
     snapshot = SimpleNamespace(manifest=manifest, decisions=[], artifacts={})
-    record = MemoryRecord(id="m1", ts=datetime(2026, 1, 1), session_id="s1",
-                          decision_seq=7, type="table_promoted", subject="t",
-                          question_context="q")
+    record = MemoryRecord(
+        id="m1", ts=datetime(2026, 1, 1), session_id="s1",
+        decision_seq=7, type="concept_clarified", subject="paziente attivo",
+        detail="flag_attivo = TRUE", question_context="q",
+    )
     monkeypatch.setattr(memory_cmd, "_load_config_or_exit", lambda path: cfg)
     monkeypatch.setattr(memory_cmd, "load_snapshot_or_exit", lambda cfg, session: snapshot)
     monkeypatch.setattr(memory_cmd, "registry_path", lambda cfg: None)

harness/tests/test_decision_min_phase.py

@@ -16,7 +16,7 @@ from tht.workflow import load_workflow
         ("concept_clarified", 1),
         ("memory_rejected", 2),
         ("question_rewritten", 3),
-        ("table_promoted", 2),
+        ("table_promoted", 4),
         ("column_corrected", 4),
         ("evidence_accepted", 4),
         ("value_grounded", 4),

harness/tests/test_memory_metadata.py

@@ -13,9 +13,9 @@ from tht.memory import MemoryRecord, memory_vector_records
 def _record(**kw) -> MemoryRecord:
     base = dict(
         id="mem-x", ts=datetime(2025, 1, 1), session_id="s", decision_seq=1,
-        type="table_promoted", subject="dim_pazienti", detail="promossa",
+        type="concept_clarified", subject="paziente attivo", detail="flag_attivo = TRUE",
         rationale="perche' serve", question_context="dammi pazienti",
-        tables=["t"], concepts=[],
+        tables=[], concepts=["paziente attivo"],
     )
     base.update(kw)
     return MemoryRecord(**base)
@@ -23,17 +23,17 @@ def _record(**kw) -> MemoryRecord:
 
 def test_memory_vector_record_has_subject_detail_rationale_in_metadata():
     vr = memory_vector_records([_record()])[0]
-    assert vr.metadata["subject"] == "dim_pazienti"
-    assert vr.metadata["detail"] == "promossa"
+    assert vr.metadata["subject"] == "paziente attivo"
+    assert vr.metadata["detail"] == "flag_attivo = TRUE"
     assert vr.metadata["rationale"] == "perche' serve"
 
 
 def test_memory_vector_record_metadata_keeps_existing_fields():
     vr = memory_vector_records([_record()])[0]
     # i campi che gia' c'erano restano (backward compat)
-    assert vr.metadata["type"] == "table_promoted"
-    assert vr.metadata["tables"] == ["t"]
-    assert vr.metadata["concepts"] == []
+    assert vr.metadata["type"] == "concept_clarified"
+    assert vr.metadata["tables"] == []
+    assert vr.metadata["concepts"] == ["paziente attivo"]
     assert vr.metadata["session_id"] == "s"
 
 
@@ -62,6 +62,6 @@ def test_save_one_memory_preserves_subject_through_upsert_row():
     save_one_memory([_record(decision_seq=1)], decision_seq=1, store=writer, embedder=embedder)
     row = writer.upsert.call_args[0][1][0]
     md = row.record.metadata
-    assert md["subject"] == "dim_pazienti"
-    assert md["detail"] == "promossa"
+    assert md["subject"] == "paziente attivo"
+    assert md["detail"] == "flag_attivo = TRUE"
     assert md["rationale"] == "perche' serve"

harness/tests/test_memory_promotion.py

@@ -9,7 +9,13 @@ la rende un passo del workflow. Questi test fissano il contratto harness-side:
 from datetime import datetime
 
 from tht.decisions import DecisionRecord, append_decision
-from tht.memory import declined_promotion_seqs, reusable_promotions
+from tht.memory import (
+    MemoryRecord,
+    declined_promotion_seqs,
+    memory_vector_records,
+    promote,
+    reusable_promotions,
+)
 from tht.session.models import SessionManifest
 from tht.workflow import load_workflow
 
@@ -62,3 +68,48 @@ def test_reusable_promotions_exclude_declined(tmp_path):
                     subject="fact_a", detail="seq:1")                         # seq 3
     cand = reusable_promotions(tmp_path, _manifest(), tmp_path / "registry.jsonl")
     assert [c.decision_seq for c in cand] == [2]
+
+
+def test_reusable_promotions_deduplicate_identical_memory_content(tmp_path):
+    append_decision(tmp_path, type="concept_clarified", subject="paziente attivo",
+                    detail="flag_attivo = TRUE", rationale="scelta reviewer")
+    append_decision(tmp_path, type="concept_clarified", subject="paziente attivo",
+                    detail="flag_attivo = TRUE", rationale="scelta reviewer")
+
+    cand = reusable_promotions(tmp_path, _manifest(), tmp_path / "registry.jsonl")
+
+    assert [c.decision_seq for c in cand] == [1]
+
+
+def test_only_concept_clarified_is_proposed_or_promoted(tmp_path):
+    append_decision(tmp_path, type="table_promoted", subject="fact_a",
+                    detail="tabella principale")
+    append_decision(tmp_path, type="table_excluded", subject="fact_b",
+                    detail="tabella non pertinente")
+    append_decision(tmp_path, type="concept_clarified", subject="paziente attivo",
+                    detail="flag_attivo = TRUE")
+    registry = tmp_path / "registry.jsonl"
+
+    candidates = reusable_promotions(tmp_path, _manifest(), registry)
+    promoted = promote(tmp_path, _manifest(), seqs=[1, 2, 3], registry_path=registry)
+
+    assert [(c.decision_seq, c.type) for c in candidates] == [(3, "concept_clarified")]
+    assert [(c.decision_seq, c.type) for c in promoted] == [(3, "concept_clarified")]
+
+
+def test_legacy_table_records_are_not_published_as_memory_vectors():
+    records = [
+        MemoryRecord(
+            id="mem-0001", ts=datetime(2026, 1, 1), session_id="s1",
+            decision_seq=1, type="table_promoted", subject="fact_a",
+        ),
+        MemoryRecord(
+            id="mem-0002", ts=datetime(2026, 1, 1), session_id="s1",
+            decision_seq=2, type="concept_clarified", subject="paziente attivo",
+            detail="flag_attivo = TRUE",
+        ),
+    ]
+
+    vectors = memory_vector_records(records)
+
+    assert [record.ref for record in vectors] == ["mem-0002"]

harness/tests/test_memory_save_one.py

@@ -16,8 +16,9 @@ from tht.memory import MemoryRecord, memory_vector_record_for_decision, save_one
 def _record(seq: int = 7, **kw) -> MemoryRecord:
     base = dict(
         id="mem-0007", ts=datetime(2025, 1, 1), session_id="s1", decision_seq=seq,
-        type="table_promoted", subject="pazienti", detail="promossa", rationale="r",
-        question_context="dammi i pazienti", tables=["pazienti"], concepts=[],
+        type="concept_clarified", subject="paziente attivo",
+        detail="flag_attivo = TRUE", rationale="r",
+        question_context="dammi i pazienti", tables=[], concepts=["paziente attivo"],
     )
     base.update(kw)
     return MemoryRecord(**base)

harness/tests/test_psd_local_compose_contract.py

@@ -54,6 +54,10 @@ def test_psd_overlay_uses_generated_workspace_for_default_and_named_commands():
     assert core["networks"]["default"]["aliases"] == ["core", "thothii-core"]
     frontend = compose["services"]["frontend"]
     assert frontend["ports"] == ["127.0.0.1:8099:8080"]
+    assert frontend["build"]["args"] == {
+        "VITE_BASE": "/",
+        "VITE_BACKEND_URL": "/api",
+    }
     assert frontend["networks"] == {
         "default": {"aliases": ["frontend", "thothii-frontend"]}
     }

harness/tests/test_solved_search_cli.py

@@ -7,10 +7,12 @@ puro (`[]` in modalita' --json) ed exit 0, cosi' il modello prosegue senza
 exemplar. Il finalize-hook gestisce gia' lo stesso scenario in modo analogo.
 """
 import json
+from datetime import datetime
 
 from typer.testing import CliRunner
 
 from tht.cli import app
+from tht.memory import MemoryRecord, save_registry
 from tht.ports.vector import VectorReadUnavailable
 from tht.vectorstore.rest_client import VectorRestError
 from tht.vectorstore.store import VectorHit
@@ -97,3 +99,44 @@ def test_solved_search_json_maps_hit_metadata(tmp_path, monkeypatch):
         "session_id": "s1", "question": "quante ablazioni nel 2023",
         "sql": "SELECT 1", "tables": ["fact_seeablazione"], "score": 0.91,
     }]
+
+
+def test_memory_search_excludes_legacy_table_records(tmp_path, monkeypatch):
+    records = [
+        MemoryRecord(
+            id="mem-0001", ts=datetime(2026, 1, 1), session_id="s1",
+            decision_seq=1, type="table_promoted", subject="fact_pazienti",
+        ),
+        MemoryRecord(
+            id="mem-0002", ts=datetime(2026, 1, 1), session_id="s1",
+            decision_seq=2, type="concept_clarified", subject="paziente attivo",
+            detail="flag_attivo = TRUE",
+        ),
+    ]
+    cfg = _cfg(tmp_path)
+    save_registry(records, tmp_path / "a" / "memory" / "registry.jsonl")
+
+    class FakeSearcher:
+        def search(self, vec, top_n=10, kinds=None):
+            return [
+                VectorHit(
+                    id=f"memory:{record.id}", kind="memory", ref=record.id,
+                    title=record.subject, content=record.detail, metadata={},
+                    similarity=0.9,
+                )
+                for record in records
+            ]
+
+    class FakeEmbedder:
+        def embed_query(self, text):
+            return [0.1] * 8
+
+    monkeypatch.setattr("tht.cli.vector_cmd.open_searcher", lambda workspace: FakeSearcher())
+    monkeypatch.setattr("tht.cli.vector_cmd.make_embedder", lambda embeddings: FakeEmbedder())
+
+    res = CliRunner().invoke(
+        app, ["memory", "search", "pazienti", "--json", "-c", str(cfg)]
+    )
+
+    assert res.exit_code == 0, res.output
+    assert [record["id"] for record in json.loads(res.stdout)] == ["mem-0002"]

harness/tht/cli/memory_cmd.py

@@ -384,7 +384,7 @@ def search_cmd(
     from rich.table import Table
 
     from tht.cli.vector_cmd import make_embedder, open_searcher, require_vector_cfg
-    from tht.memory import decided_memory_ids, load_registry
+    from tht.memory import REUSABLE_TYPES, decided_memory_ids, load_registry
 
     cfg = _load_config_or_exit(config)
     require_vector_cfg(cfg)
@@ -401,6 +401,8 @@ def search_cmd(
         rec = by_id.get(h.ref)
         if rec is None:
             continue  # indice piu' avanti del registro: ignora
+        if rec.type not in REUSABLE_TYPES:
+            continue  # record legacy non-concept: non e' una memory riusabile
         if rec.id in excluded:
             continue  # gia' decisa in questa sessione: non riproporla
         results.append({

harness/tht/memory.py

@@ -115,14 +115,6 @@ def delete_record(path: Path, mem_id: str) -> MemoryRecord:
     raise MemoryNotFound(mem_id)
 
 
-def _default_tables(decision: DecisionRecord) -> list[str]:
-    if decision.type in ("table_promoted", "table_excluded"):
-        return [decision.subject]
-    if decision.type in ("column_corrected", "join_modified") and "." in decision.subject:
-        return [decision.subject.split(".")[0]]
-    return []
-
-
 def _default_concepts(decision: DecisionRecord) -> list[str]:
     if decision.type == "concept_clarified":
         return [decision.subject]
@@ -139,9 +131,9 @@ def _next_id_num(existing: list[MemoryRecord]) -> int:
     return (max(nums) + 1) if nums else 1
 
 
-# Solo questi tipi di decisione sono concetti riusabili in altre generazioni
-# (scelta reviewer): il resto e' query-specifico e non va proposto in promozione.
-REUSABLE_TYPES = frozenset({"concept_clarified", "table_promoted", "table_excluded"})
+# Solo i concetti chiariti sono riusabili tra domande. Le scelte sulle tabelle
+# dipendono dallo schema-linking della singola domanda e non sono memory.
+REUSABLE_TYPES = frozenset({"concept_clarified"})
 MAX_PROMOTION_CANDIDATES = 5
 
 
@@ -159,6 +151,8 @@ def _compute_promotions(
     context = question_context(decisions, manifest)
     out: list[MemoryRecord] = []
     for d in selected:
+        if d.type not in REUSABLE_TYPES:
+            continue
         if (manifest.id, d.seq) in already:
             continue
         out.append(
@@ -166,7 +160,7 @@ def _compute_promotions(
                 id=f"mem-{n:04d}", ts=datetime.now(UTC), session_id=manifest.id,
                 decision_seq=d.seq, type=d.type, subject=d.subject, detail=d.detail,
                 rationale=d.rationale, question_context=context,
-                tables=_default_tables(d), concepts=_default_concepts(d),
+                tables=[], concepts=_default_concepts(d),
             )
         )
         n += 1
@@ -206,9 +200,10 @@ def reusable_promotions(
         session_dir, manifest, seqs=None, existing=load_registry(registry_path)
     )
     declined = declined_promotion_seqs(effective_decisions(session_dir))
-    return [
+    reusable = [
         c for c in cand if c.type in REUSABLE_TYPES and c.decision_seq not in declined
     ]
+    return _dedupe_reusable_promotions(reusable)
 
 
 def reusable_promotions_snapshot(snapshot, registry_path: Path) -> list[MemoryRecord]:
@@ -216,7 +211,30 @@ def reusable_promotions_snapshot(snapshot, registry_path: Path) -> list[MemoryRe
 
     cand = _compute_promotions(snapshot, snapshot.manifest, seqs=None, existing=load_registry(registry_path))
     declined = declined_promotion_seqs(effective_decisions(snapshot))
-    return [c for c in cand if c.type in REUSABLE_TYPES and c.decision_seq not in declined]
+    reusable = [c for c in cand if c.type in REUSABLE_TYPES and c.decision_seq not in declined]
+    return _dedupe_reusable_promotions(reusable)
+
+
+def _dedupe_reusable_promotions(records: list[MemoryRecord]) -> list[MemoryRecord]:
+    """Keep the first proposal for identical reviewer-visible memory content."""
+    seen: set[tuple[str, str, str, str, str]] = set()
+    out: list[MemoryRecord] = []
+    for record in records:
+        key = tuple(
+            " ".join(value.split()).casefold()
+            for value in (
+                record.type,
+                record.subject,
+                record.detail,
+                record.rationale,
+                record.question_context,
+            )
+        )
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(record)
+    return out
 
 
 def preview_promotions(
@@ -235,6 +253,8 @@ def preview_promotions_snapshot(snapshot, registry_path: Path) -> list[MemoryRec
 def memory_vector_records(records: list[MemoryRecord]) -> list[VectorRecord]:
     out: list[VectorRecord] = []
     for r in records:
+        if r.type not in REUSABLE_TYPES:
+            continue
         lines = [
             f"Decisione {r.type}: {r.subject}",
             r.detail,
@@ -268,10 +288,12 @@ def memory_vector_record_for_decision(
     D11 save-one builds only this one record (not the full memory_vector_records
     list) so the remote upsert is a single row.
     """
-    match = [r for r in records if r.decision_seq == decision_seq]
-    if not match:
+    vectors = memory_vector_records(
+        [r for r in records if r.decision_seq == decision_seq]
+    )
+    if not vectors:
         return None
-    return memory_vector_records(match)[0]
+    return vectors[0]
 
 
 def save_one_memory(

harness/workflow.yaml

@@ -21,7 +21,7 @@ phases:
     advance: auto_if_empty
     prerequisites: []
     artifacts_out: []
-    emits: [memory_rejected, table_promoted, table_excluded]
+    emits: [memory_rejected, concept_clarified]
   - id: F3
     name: riscrittura
     advance: kind:phase